import os
import paddle
"""
Samples Generation codes
Reference with https://github.com/FoundationVision/LlamaGen/blob/main/autoregressive/models/generate.py
"""
import argparse, sys, glob
import time
import numpy as np
from omegaconf import OmegaConf
from PIL import Image
from tqdm import tqdm, trange
from einops import repeat
import importlib
from taming.modules.transformer.gpt import sample
import time

rescale = lambda x: (x + 1.0) / 2.0


def get_obj_from_str(string, reload=False):
    print(string)
    module, cls = string.rsplit('.', 1)
    if reload:
        module_imp = importlib.import_module(module)
        importlib.reload(module_imp)
    return getattr(importlib.import_module(module, package=None), cls)


def instantiate_from_config(config):
    if not 'class_path' in config:
        raise KeyError('Expected key `class_path` to instantiate.')
    return get_obj_from_str(config['class_path'])(**config.get('init_args',
        dict()))


def chw_to_pillow(x):
    return Image.fromarray((255 * rescale(x.detach().cpu().numpy().
        transpose(1, 2, 0))).clip(0, 255).astype(np.uint8))


@paddle.no_grad()
def sample_classconditional(model, batch_size, class_label, steps=256,
    temperature=None, top_k=None, callback=None, dim_z=18, h=16, w=16,
    verbose_time=False, top_p=None, token_factorization=False, cfg_scale=1.0):
    log = dict()
    assert type(class_label
        ) == int, f'expecting type int but type is {type(class_label)}'
    assert not model.be_unconditional, 'Expecting a class-conditional Net2NetTransformer.'
    c_indices = repeat(paddle.to_tensor(data=[class_label]), '1 -> b 1', b=
        batch_size)
    if cfg_scale[0] > 1.0:
        cond_null = paddle.ones_like(x=c_indices
            ) * model.transformer.config.class_num
        cond_combined = paddle.concat(x=[c_indices, cond_null], axis=0)
    else:
        cond_combined = c_indices
    qzshape = [batch_size, dim_z, h, w]
    t1 = time.time()
    index_sample = sample(cond_combined, model.transformer, steps=steps,
        sample_logits=True, top_k=top_k, callback=callback, temperature=
        temperature, top_p=top_p, token_factorization=token_factorization,
        cfg_scale=cfg_scale)

    if verbose_time:
        sampling_time = time.time() - t1
        print(f'Full sampling takes about {sampling_time:.2f} seconds.')
    x_sample = model.decode_to_img(index_sample, qzshape)
    log['samples'] = x_sample
    log['class_label'] = c_indices
    return log


@paddle.no_grad()
def run(logdir, model, batch_size, temperature, top_k, unconditional=True,
    num_samples=50000, given_classes=None, top_p=None, token_factorization=
    True, cfg_scale=1.0):
    batches = [batch_size for _ in range(num_samples // batch_size)] + [
        num_samples % batch_size]
    if not unconditional:
        assert given_classes is not None
        print(
            f'Running in pure class-conditional sampling mode. I will produce {num_samples} samples for each of the {len(given_classes)} classes, i.e. {num_samples * len(given_classes)} in total.'
            )
        for class_label in tqdm(given_classes, desc='Classes'):
            for n, bs in tqdm(enumerate(batches), desc='Sampling Class'):
                if bs == 0:
                    break
                logs = sample_classconditional(model, batch_size=bs,
                    class_label=class_label, temperature=temperature, top_k
                    =top_k, top_p=top_p, token_factorization=
                    token_factorization, cfg_scale=cfg_scale)
                save_from_logs(logs, logdir, base_count=n * batch_size,
                    cond_key=logs['class_label'])


def save_from_logs(logs, logdir, base_count, key='samples', cond_key=None):
    xx = logs[key]
    for i, x in enumerate(xx):
        x = chw_to_pillow(x)
        count = base_count + i
        if cond_key is None:
            x.save(os.path.join(logdir, f'{count:06}.png'))
        else:
            condlabel = cond_key[i]
            if type(condlabel) == paddle.Tensor:
                condlabel = condlabel.item()
            os.makedirs(os.path.join(logdir, str(condlabel)), exist_ok=True)
            x.save(os.path.join(logdir, str(condlabel), f'{count:06}.png'))


def get_parser():

    def str2bool(v):
        if isinstance(v, bool):
            return v
        if v.lower() in ('yes', 'true', 't', 'y', '1'):
            return True
        elif v.lower() in ('no', 'false', 'f', 'n', '0'):
            return False
        else:
            raise argparse.ArgumentTypeError('Boolean value expected.')
    parser = argparse.ArgumentParser()
    parser.add_argument('--ckpt', type=str, nargs='?', help=
        'load from logdir or checkpoint in logdir')
    parser.add_argument('-o', '--outdir', type=str, nargs='?', help=
        'path where the samples will be logged to.', default='')
    parser.add_argument('--config', nargs='*', metavar='config.yaml', help=
        'paths to base configs. Loaded from left-to-right. Parameters can be overwritten or added with command-line options of the form `--key value`.'
        , default=list())
    parser.add_argument('-n', '--num_samples', type=int, nargs='?', help=
        'num_samples to draw', default=50000)
    parser.add_argument('--batch_size', type=int, nargs='?', help=
        'the batch size', default=25)
    parser.add_argument('-k', '--top_k', type=str, nargs='?', help=
        'top-k value to sample with', default=250)
    parser.add_argument('-t', '--temperature', type=str, nargs='?', help=
        'temperature value to sample with', default=1.0)
    parser.add_argument('-p', '--top_p', type=str, nargs='?', help=
        'top-p value to sample with', default=1.0)
    parser.add_argument('--classes', type=str, nargs='?', help=
        'specify comma-separated classes to sample from. Uses 1000 classes per default.'
        , default='imagenet')
    parser.add_argument('--token_factorization', action='store_true', help=
        'whether to use token factorization')
    parser.add_argument('--cfg_scale', type=str, default=1.0)
    return parser


def load_model_from_config(config, sd, gpu=True, eval_mode=True):
    model = instantiate_from_config(config)
    
    if sd is not None:
        model.set_state_dict(state_dict=sd)
    if gpu:
        model = model
    if eval_mode:
        model.eval()
    return {'model': model}


def load_model(config, ckpt, gpu, eval_mode):
    if ckpt:
        pl_sd = paddle.load(path=str(ckpt))
        global_step = pl_sd.get('global_step', None)
        if global_step:
            print(f'loaded model from global step {global_step}.')
    else:
        pl_sd = {'state_dict': None}
        global_step = None
    model = load_model_from_config(config.model, pl_sd, gpu=
        gpu, eval_mode=eval_mode)['model']
    return model, global_step


if __name__ == '__main__':
    sys.path.append(os.getcwd())
    parser = get_parser()
    opt, unknown = parser.parse_known_args()
    logdir = opt.outdir
    ckpt = opt.ckpt
    ckpt_name = ckpt.split('/')[-1]
    config = OmegaConf.load(opt.config[0])
    model, global_step = load_model(config, ckpt, gpu=True, eval_mode=True)
    opt.top_k = [int(topk) for topk in opt.top_k.split(',')]
    opt.top_p = [float(topp) for topp in opt.top_p.split(',')]
    opt.temperature = [float(temp) for temp in opt.temperature.split(',')]
    opt.cfg_scale = [float(cfg_scal) for cfg_scal in opt.cfg_scale.split(',')]
    if opt.classes == 'imagenet':
        given_classes = [i for i in range(1000)]
    else:
        cls_str = opt.classes
        assert not cls_str.endswith(','
            ), 'class string should not end with a ","'
        given_classes = [int(c) for c in cls_str.split(',')]
    logdir = os.path.join(logdir, 'samples',
        f'top_k_{opt.top_k[0]}_{opt.top_k[1]}_temp_{opt.temperature[0]:.2f}_{opt.temperature[1]:.2f}_top_p_{opt.top_p[0]}_{opt.top_p[1]}_cfg_{opt.cfg_scale[0]}_{opt.cfg_scale[1]}'
        , f'{ckpt_name}')
    print(f'Logging to {logdir}')
    os.makedirs(logdir, exist_ok=True)
    start_time = time.time()
    run(logdir, model, opt.batch_size, opt.temperature, opt.top_k,
        unconditional=model.be_unconditional, given_classes=given_classes,
        num_samples=opt.num_samples, top_p=opt.top_p, token_factorization=
        opt.token_factorization, cfg_scale=opt.cfg_scale)
    end_time = time.time()
    print(end_time - start_time, 's')
    print('done.')
